Table of Contents

Overview

XXX from the XXX Research Team is looking to engage in consultation with the Telethon Kids Instituteā€™s Biometrics team to undertake statistical analysis to determine brief project summary.

Project roles:

Study Overview

Study overview to put the analysis in context.

Research Questions (RQ)

  1. Research Question 1
  2. Research Question 2
  3. Research Question 3

Inclusion Criteria

  • Age < 18 years old
  • Scores > 20
  • ā€¦

Data Variables Overview

Key Variables

  • Outcome variables (to be modelled separately)
    • XXX
    • XXX

Primary Independent Variable

  • XXX

Adjusting Covariates

  • XXX
  • XXX
  • XXX

Dataset Overview

  • file.xlsx (DD MMM YYYY, provided by XXX)
  • file.sav (DD MMM YYYY, provided by XXX)

Preliminary Data Cleaning Steps

  • XXX
  • XXX

Cohort Summary

There were XXX children born from XXX women over the study study interval, XXX were males that were diagnosed with XXX.

Pairs Plot

Actions

  • XXX to provide cleaned/recoded data to XXX
  • XXX could not be found in the provided data, could XXX please provide some insight

Analysis Plan

  • Research Question 1
    • Linear regression lm() (R Core Team 2019)
      • yā€„āˆ¼ā€„mxā€…+ā€…b
      • where, XXX
  • Research Question 2
    • Linear regression glm() (R Core Team 2019)
      • yā€„āˆ¼ā€„mxā€…+ā€…b
      • where, XXX

Statistical models will be prepared for the dependant measures identified above with some commentary regarding their interpretation and statistical significance in terms of 95% confidence intervals. Where appropriate, figures will be prepared to help convey the analysis findings. Descriptive statistics can also be provided in the final result/manuscript preparation upon request.

Commentary around the methods and results of model creation will be provided in the form of a report that will include bullet point overviews of model preparation and results. Once specific analysis outcomes have been identified by the project sponsor for inclusion in publishable works, ā€œcopy-and-pasteā€ paragraphs for manuscript preparation and a more detailed analysis summary can be provided. The analysis and reporting will be completed in the R programming language and all R script files associated with the analysis will be made available to the researcher upon request.

Analysis Estimate

Assuming the data are tidy and clean, we estimate 4-6 days of analysis time (@$875 excluding GST, per day), which does not include final result preparation time. Up to two Biometrics Biostatisticians to be included as authors on resulting publication(s) (assuming sufficient academic contributions are made).

It would be worthwhile preparing a skeleton of the tables for the paper to make the most efficient use of the analysis time, as working through multiple iterations of possible tables (which we are happy to do if required) will increase the analysis time/cost.

Final table preparation and contribution to paper writing, within reason, is provided in-kind. Cost recovery for this function will be necessary if there are substantial edits/revisions/changes-of-mind.

End Matter

Reproducible Research Information

This document was prepared using the software R (R Core Team 2019), via the RStudio IDE (RStudio Team 2016), and was written in RMarkdown (Allaire et al. 2019).

sessionInfo()
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 8.1 x64 (build 9600)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252   
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C                      
## [5] LC_TIME=English_Australia.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] rmarkdown_1.16        captioner_2.2.3       jtools_2.0.1         
##  [4] GGally_1.4.0          broom_0.5.2           kableExtra_1.1.0     
##  [7] knitr_1.25            biometrics_1.0.4      ProjectTemplate_0.9.0
## [10] lubridate_1.7.4       forcats_0.4.0         stringr_1.4.0        
## [13] tibble_2.1.3          purrr_0.3.2           readr_1.3.1          
## [16] tidyr_1.0.0           dplyr_0.8.3           ggplot2_3.2.1        
## [19] repmis_0.5           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_0.2.5   xfun_0.9           reshape2_1.4.3    
##  [4] pander_0.6.3       lattice_0.20-38    colorspace_1.4-1  
##  [7] vctrs_0.2.0        generics_0.0.2     viridisLite_0.3.0 
## [10] htmltools_0.3.6    yaml_2.2.0         rlang_0.4.0       
## [13] R.oo_1.22.0        pillar_1.4.2       glue_1.3.1        
## [16] withr_2.1.2        R.utils_2.9.0      RColorBrewer_1.1-2
## [19] R.cache_0.13.0     lifecycle_0.1.0    plyr_1.8.4        
## [22] munsell_0.5.0      gtable_0.3.0       rvest_0.3.4       
## [25] R.methodsS3_1.7.1  evaluate_0.14      labeling_0.3      
## [28] Rcpp_1.0.2         scales_1.0.0       backports_1.1.5   
## [31] webshot_0.5.1      hms_0.5.1          digest_0.6.21     
## [34] stringi_1.4.3      grid_3.6.1         tools_3.6.1       
## [37] magrittr_1.5       lazyeval_0.2.2     crayon_1.3.4      
## [40] pkgconfig_2.0.3    zeallot_0.1.0      xml2_1.2.2        
## [43] data.table_1.12.2  reshape_0.8.8      assertthat_0.2.1  
## [46] httr_1.4.1         rstudioapi_0.10    R6_2.4.0          
## [49] igraph_1.2.4.1     nlme_3.1-141       compiler_3.6.1

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2019. Rmarkdown: Dynamic Documents for R. https://CRAN.R-project.org/package=rmarkdown.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

RStudio Team. 2016. RStudio: Integrated Development Environment for R. Boston, MA: RStudio, Inc. http://www.rstudio.com/.